MapReduces计数实验

实验内容

现有某电商网站用户对商品的收藏数据，记录了用户收藏的商品id以及收藏日期，名为buyer_favorite1。

buyer_favorite1包含：买家id，商品id，收藏日期这三个字段，数据以“\t”分割，样本数据及格式如下：

买家id   商品id    收藏日期  

10181   1000481   2010-04-04 16:54:31  

20001   1001597   2010-04-07 15:07:52  

20001   1001560   2010-04-07 15:08:27  

20042   1001368   2010-04-08 08:20:30  

20067   1002061   2010-04-08 16:45:33  

20056   1003289   2010-04-12 10:50:55  

20056   1003290   2010-04-12 11:57:35  

20056   1003292   2010-04-12 12:05:29  

20054   1002420   2010-04-14 15:24:12  

20055   1001679   2010-04-14 19:46:04  

20054   1010675   2010-04-14 15:23:53  

20054   1002429   2010-04-14 17:52:45  

20076   1002427   2010-04-14 19:35:39  

20054   1003326   2010-04-20 12:54:44  

20056   1002420   2010-04-15 11:24:49  

20064   1002422   2010-04-15 11:35:54  

20056   1003066   2010-04-15 11:43:01  

20056   1003055   2010-04-15 11:43:06  

20056   1010183   2010-04-15 11:45:24  

20056   1002422   2010-04-15 11:45:49  

20056   1003100   2010-04-15 11:45:54  

20056   1003094   2010-04-15 11:45:57  

20056   1003064   2010-04-15 11:46:04  

20056   1010178   2010-04-15 16:15:20  

20076   1003101   2010-04-15 16:37:27  

20076   1003103   2010-04-15 16:37:05  

20076   1003100   2010-04-15 16:37:18  

20076   1003066   2010-04-15 16:37:31  

20054   1003103   2010-04-15 16:40:14  

20054   1003100   2010-04-15 16:40:16  

源代码

package shiyan;

import java.io.IOException;

import java.util.StringTokenizer;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class WordCount {
	public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
		Job job = Job.getInstance();
		job.setJobName("WordCount");
		job.setJarByClass(WordCount.class);
		job.setMapperClass(doMapper.class);
		job.setReducerClass(doReducer.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(IntWritable.class);
		Path in = new Path("hdfs://localhost:9000/mymapreduce1/in/buy");
		Path out = new Path("hdfs://localhost:9000/mymapreduce1/out");
		FileInputFormat.addInputPath(job, in);
		FileOutputFormat.setOutputPath(job, out);
		System.exit(job.waitForCompletion(true) ? 0 : 1);
	}

	public static class doMapper extends Mapper<Object, Text, Text, IntWritable> {
		public static final IntWritable one = new IntWritable(1);
		public static Text word = new Text();

		@Override
		protected void map(Object key, Text value, Context context) throws IOException, InterruptedException {
			StringTokenizer tokenizer = new StringTokenizer(value.toString(), " ");
			word.set(tokenizer.nextToken());
			context.write(word, one);
		}
	}

	public static class doReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
		private IntWritable result = new IntWritable();

		@Override
		protected void reduce(Text key, Iterable<IntWritable> values, Context context)
				throws IOException, InterruptedException {
			int sum = 0;
			for (IntWritable value : values) {
				sum += value.get();
			}
			result.set(sum);
			context.write(key, result);
		}
	}
}

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